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Merge pull request #323 from gcarq/add_indicators
Add 28 optional indicators populate_indicators()
This commit is contained in:
Gérald LONLAS
GitHub
commit
7e233b536c
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@ -11,7 +11,7 @@ import talib.abstract as ta
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from pandas import DataFrame, to_datetime
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from freqtrade.exchange import get_ticker_history
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from freqtrade.vendor.qtpylib.indicators import awesome_oscillator, crossed_above
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from freqtrade.vendor.qtpylib.indicators import awesome_oscillator, PandasObject as qtpylib
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logger = logging.getLogger(__name__)
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@ -40,34 +40,185 @@ def parse_ticker_dataframe(ticker: list) -> DataFrame:
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def populate_indicators(dataframe: DataFrame) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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Performance Note: For the best performance be frugal on the number of indicators
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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"""
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dataframe['sar'] = ta.SAR(dataframe)
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# Momentum Indicator
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# ------------------------------------
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# ADX
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dataframe['adx'] = ta.ADX(dataframe)
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stoch = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch['fastd']
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dataframe['fastk'] = stoch['fastk']
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dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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dataframe['mfi'] = ta.MFI(dataframe)
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dataframe['rsi'] = ta.RSI(dataframe)
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dataframe['cci'] = ta.CCI(dataframe)
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dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
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dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
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dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
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dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
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# Awesome oscillator
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dataframe['ao'] = awesome_oscillator(dataframe)
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"""
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# Commodity Channel Index: values Oversold:<-100, Overbought:>100
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dataframe['cci'] = ta.CCI(dataframe)
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"""
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# MACD
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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dataframe['macdhist'] = macd['macdhist']
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# MFI
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dataframe['mfi'] = ta.MFI(dataframe)
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# Minus Directional Indicator / Movement
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dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# Plus Directional Indicator / Movement
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dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
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dataframe['plus_di'] = ta.PLUS_DI(dataframe)
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"""
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# ROC
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dataframe['roc'] = ta.ROC(dataframe)
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"""
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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"""
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# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
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rsi = 0.1 * (dataframe['rsi'] - 50)
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dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
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# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
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dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
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# Stoch
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stoch = ta.STOCH(dataframe)
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dataframe['slowd'] = stoch['slowd']
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dataframe['slowk'] = stoch['slowk']
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"""
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# Stoch fast
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stoch_fast = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch_fast['fastd']
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dataframe['fastk'] = stoch_fast['fastk']
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"""
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# Stoch RSI
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stoch_rsi = ta.STOCHRSI(dataframe)
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dataframe['fastd_rsi'] = stoch_rsi['fastd']
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dataframe['fastk_rsi'] = stoch_rsi['fastk']
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"""
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# Overlap Studies
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# ------------------------------------
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# Previous Bollinger bands
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# Because ta.BBANDS implementation is broken with small numbers, it actually
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# returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
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# and use middle band instead.
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dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
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"""
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# Bollinger bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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"""
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# EMA - Exponential Moving Average
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dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
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dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
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dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
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dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
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# SAR Parabol
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dataframe['sar'] = ta.SAR(dataframe)
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# SMA - Simple Moving Average
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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# TEMA - Triple Exponential Moving Average
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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# Cycle Indicator
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# ------------------------------------
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# Hilbert Transform Indicator - SineWave
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hilbert = ta.HT_SINE(dataframe)
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dataframe['htsine'] = hilbert['sine']
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dataframe['htleadsine'] = hilbert['leadsine']
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dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
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dataframe['plus_di'] = ta.PLUS_DI(dataframe)
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dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# Pattern Recognition - Bullish candlestick patterns
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# ------------------------------------
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"""
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# Hammer: values [0, 100]
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dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
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# Inverted Hammer: values [0, 100]
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dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
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# Dragonfly Doji: values [0, 100]
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dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
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# Piercing Line: values [0, 100]
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dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
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# Morningstar: values [0, 100]
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dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
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# Three White Soldiers: values [0, 100]
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dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
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"""
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# Pattern Recognition - Bearish candlestick patterns
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# ------------------------------------
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"""
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# Hanging Man: values [0, 100]
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dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
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# Shooting Star: values [0, 100]
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dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
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# Gravestone Doji: values [0, 100]
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dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
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# Dark Cloud Cover: values [0, 100]
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dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
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# Evening Doji Star: values [0, 100]
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dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
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# Evening Star: values [0, 100]
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dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
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"""
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# Pattern Recognition - Bullish/Bearish candlestick patterns
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# ------------------------------------
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"""
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# Three Line Strike: values [0, -100, 100]
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dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
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# Spinning Top: values [0, -100, 100]
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dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
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# Engulfing: values [0, -100, 100]
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dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
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# Harami: values [0, -100, 100]
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dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
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# Three Outside Up/Down: values [0, -100, 100]
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dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
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# Three Inside Up/Down: values [0, -100, 100]
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dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
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"""
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# Chart type
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# ------------------------------------
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"""
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# Heikinashi stategy
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heikinashi = qtpylib.heikinashi(dataframe)
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dataframe['ha_open'] = heikinashi['open']
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dataframe['ha_close'] = heikinashi['close']
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dataframe['ha_high'] = heikinashi['high']
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dataframe['ha_low'] = heikinashi['low']
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"""
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return dataframe
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@ -102,8 +253,8 @@ def populate_sell_trend(dataframe: DataFrame) -> DataFrame:
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dataframe.loc[
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(
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(
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(crossed_above(dataframe['rsi'], 70)) |
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(crossed_above(dataframe['fastd'], 70))
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(qtpylib.crossed_above(dataframe['rsi'], 70)) |
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(qtpylib.crossed_above(dataframe['fastd'], 70))
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) &
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(dataframe['adx'] > 10) &
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(dataframe['minus_di'] > 0)
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